Open Access
Issue |
A&A
Volume 695, March 2025
|
|
---|---|---|
Article Number | A166 | |
Number of page(s) | 25 | |
Section | Cosmology (including clusters of galaxies) | |
DOI | https://doi.org/10.1051/0004-6361/202451779 | |
Published online | 18 March 2025 |
- Adame, A. G., Aguilar, J., Ahlen, S., et al. 2025, JCAP, 02, 021 [CrossRef] [Google Scholar]
- Akaike, H. 1974, IEEE Trans. Autom. Control., 19, 716 [NASA ADS] [CrossRef] [Google Scholar]
- Alfano, A. C., Luongo, O., & Muccino, M. 2024, JCAP, 2024, 055 [CrossRef] [Google Scholar]
- Bamba, K., Capozziello, S., Nojiri, S., & Odintsov, S. D. 2012, Ap&SS, 342, 155 [NASA ADS] [CrossRef] [Google Scholar]
- Barboza, E., & Alcaniz, J. 2008, Phys. Rev. Lett. B, 666, 415 [Google Scholar]
- Basilakos, S., & Plionis, M. 2009, A&A, 507, 47 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Benaoum, H. B. 2012, Adv. High Energy Phys., 2012, 357802 [Google Scholar]
- Benetti, M., & Capozziello, S. 2019, JCAP, 12, 008 [CrossRef] [Google Scholar]
- Bentley, J. L. 1975, Commun. ACM, 18, 509 [CrossRef] [Google Scholar]
- Bento, M., Bertolami, O., & Sen, A. A. 2002, Phys. Rev. D, 66, 043507 [CrossRef] [Google Scholar]
- Bhatia, N. 2010, Int. J. Com. Sci. Inf. Sec., 8, 302 [Google Scholar]
- Bolstad, W. M. 2009, Understanding Computational Bayesian Statistics (John Wiley& Sons), 644 [CrossRef] [Google Scholar]
- Breiman, L. 2001, Mach. Learn., 45, 5 [Google Scholar]
- Brescia, M., Cavuoti, S., & Longo, G. 2015, MNRAS, 450, 3893 [NASA ADS] [CrossRef] [Google Scholar]
- Brescia, M., Salvato, M., Cavuoti, S., et al. 2019, MNRAS, 489, 663 [NASA ADS] [CrossRef] [Google Scholar]
- Brockwell, P. J., & Davis, R. A. 2002, Introduction to Time Series and Forecasting (Springer) [CrossRef] [Google Scholar]
- Brooks, S., Gelman, A., Jones, G., & Meng, X.-L. 2011, Handbook of Markov Chain Monte Carlo (CRC Press) [CrossRef] [Google Scholar]
- Cai, Y.-F., Capozziello, S., De Laurentis, M., & Saridakis, E. N. 2016, Rep. Prog. Phys., 79, 106901 [Google Scholar]
- Capozziello, S., & De Laurentis, M. 2011, Phys. Rep., 509, 167 [CrossRef] [Google Scholar]
- Capozziello, S., D’Agostino, R., & Luongo, O. 2018, MNRAS, 476, 3924 [NASA ADS] [CrossRef] [Google Scholar]
- Capozziello, S., D’Agostino, R., & Luongo, O. 2020, MNRAS, 494, 2576 [NASA ADS] [CrossRef] [Google Scholar]
- Capozziello, S., Dunsby, P. K. S., & Luongo, O. 2021, MNRAS, 509, 5399 [NASA ADS] [CrossRef] [Google Scholar]
- Cardone, V. F., Troisi, A., & Capozziello, S. 2004, Phys. Rev. D, 69, 083517 [Google Scholar]
- Carloni, Y., Luongo, O., & Muccino, M. 2025, Phys. Rev. D, 111, 023512 [Google Scholar]
- Carpenter, B., Gelman, A., Hoffman, M. D., et al. 2017, J. Stat. Softw., 76, 1 [NASA ADS] [CrossRef] [Google Scholar]
- Chevallier, M., & Polarski, D. 2001, Int. J. Mod. Phys. D, 10, 213 [Google Scholar]
- Colgáin, E. Ó., Dainotti, M. G., Capozziello, S., et al. 2024, ArXiv e-prints [arXiv:2404.08633] [Google Scholar]
- Cover, T., & Hart, P. 1967, IEEE Trans. Inf. Theory, 13, 21 [CrossRef] [Google Scholar]
- Czerwinska, U. 2020, Push the Limits of Explainability – an Ultimate Guide to SHAP Library, https://urszulaczerwinska.github.io/works/interpretability-shap [Google Scholar]
- Demianski, M., Piedipalumbo, E., Rubano, C., & Scudellaro, P. 2012, MNRAS, 426, 1396 [Google Scholar]
- D’Isanto, A., Cavuoti, S., Brescia, M., et al. 2016, MNRAS, 457, 3119 [CrossRef] [Google Scholar]
- Dunsby, P. K., & Luongo, O. 2016, Int. J. Geom. Meth. Mod. Phys., 13, 1630002 [NASA ADS] [CrossRef] [Google Scholar]
- Escamilla-Rivera, C., & Capozziello, S. 2019, Int. J. Mod. Phys. D, 28, 1950154 [NASA ADS] [CrossRef] [Google Scholar]
- Escamilla-Rivera, C., Quintero, M. A. C., & Capozziello, S. 2020, JCAP, 2020, 008 [CrossRef] [Google Scholar]
- Fabris, J. C., Velten, H. E. S., Ogouyandjou, C., & Tossa, J. 2011, Phys. Rev. Lett. B, 694, 289 [Google Scholar]
- Falk, M., Marohn, F., Michel, R., et al. 2011, A First Course on Time Series Analysis: Examples with SAS (epubli GmbH) [Google Scholar]
- Feroz, F., & Hobson, M. P. 2008, MNRAS, 384, 449 [NASA ADS] [CrossRef] [Google Scholar]
- Fisher, R. A. 1922, Phil. Trans. R. Soc. A, 222, 309 [Google Scholar]
- Foreman-Mackey, D., Hogg, D. W., Lang, D., & Goodman, J. 2013, PASP, 125, 306 [Google Scholar]
- Friedman, J. H. 2001, Ann. Stat., 29, 1189 [Google Scholar]
- Gelman, A., & Rubin, D. B. 1992, Stat. Sci., 7, 457 [Google Scholar]
- Geyer, C. J. 1992, Stat. Sci., 7, 473 [Google Scholar]
- Goodman, J., & Weare, J. 2010, Commun. App. Math. Comp. Sci., 5, 65 [NASA ADS] [CrossRef] [Google Scholar]
- Gregory, P. 2005, Bayesian Logical Data Analysis for the Physical Sciences: A Comparative Approach with Mathematica Support (Cambridge: Cambridge University Press) [CrossRef] [Google Scholar]
- Guy, J., Astier, P., Baumont, S., et al. 2007, A&A, 466, 11 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- Hastie, T., Tibshirani, R., Friedman, J., et al. 2009, The Elements of Statistical Learning: Data Mining, Inference and Prediction (Springer) [Google Scholar]
- Heavens, A., Fantaye, Y., Mootoovaloo, A., et al. 2017, ArXiv e-prints [arXiv:1704.03472] [Google Scholar]
- Higson, E., Handley, W., Hobson, M., & Lasenby, A. 2018, Bayesian Anal., 13, 873 [NASA ADS] [CrossRef] [Google Scholar]
- Higson, E., Handley, W., Hobson, M., & Lasenby, A. 2019, Stat. Comput., 29, 891 [Google Scholar]
- Hogg, D. W., Bovy, J., & Lang, D. 2010, ArXiv e-prints [arXiv:1008.4686] [Google Scholar]
- Huterer, D., & Turner, M. S. 2001, Phys. Rev. D, 64, 123527 [Google Scholar]
- Keeton, C. R. 2011, MNRAS, 414, 1418 [Google Scholar]
- Kingma, D. P., & Ba, J. 2014, ArXiv e-prints [arXiv:1412.6980] [Google Scholar]
- Kursa, M. B., & Rudnicki, W. R. 2011, ArXiv e-prints [arXiv:1106.5112] [Google Scholar]
- Lartillot, N., & Philippe, H. 2006, Syst. Biol., 55, 195 [Google Scholar]
- Li, Q. 2019, Shap-shapley, https://github.com/shaoshanglqy/shap-shapley [Google Scholar]
- Liaw, A., & Wiener, M. 2002, R News, 2, 18 [Google Scholar]
- Linder, E. V. 2008, Gen. Relativ. Gravit., 40, 329 [CrossRef] [Google Scholar]
- Lomb, N. R. 1976, Astrophys. Space Sci., 39, 447 [Google Scholar]
- Lundberg, S. M., & Lee, S.-I. 2017, Adv. Neural. Inf. Process. Syst., 30, 4768 [Google Scholar]
- Lundberg, S. M., Erion, G., Chen, H., et al. 2020, Nat. Mach. Intell., 2, 56 [CrossRef] [Google Scholar]
- Luongo, O., & Muccino, M. 2024, A&A, 690, A40 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
- MacKay, D. J. 2003, Information Theory, Inference and Learning Algorithms (Cambridge: Cambridge University Press) [Google Scholar]
- Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E. 1953, J. Chem. Phys., 21, 1087 [Google Scholar]
- Nami, Y. 2021, Why Does Increasing k Decrease Variance in kNN?, https://towardsdatascience.com/why-does-increasing-k-decrease-variance-in-knn-9ed6de2f5061 [Google Scholar]
- Norris, J. R. 1998, Markov Chains (Cambridge: Cambridge University Press) [Google Scholar]
- Nun, I., Protopapas, P., Sim, B., et al. 2015, ArXiv e-prints [arXiv:1506.00010] [Google Scholar]
- Orchard, L., & Cárdenas, V. H. 2024, Phys. Dark Univ., 46, 101678 [Google Scholar]
- Ostriker, J. P., & Vishniac, E. T. 1986, ApJ, 306, L51 [Google Scholar]
- Paul, B., & Thakur, P. 2013, J. Cosmol. Astropart. Phys., 2013, 052 [CrossRef] [Google Scholar]
- Pedregosa, F., Varoquaux, G., Gramfort, A., et al. 2011, J. Mach. Learn. Res., 12, 2825 [Google Scholar]
- Peebles, P. J. E., & Ratra, B. 2003, Rev. Mod. Phys., 75, 559 [NASA ADS] [CrossRef] [Google Scholar]
- Perlmutter, S., Aldering, G., Goldhaber, G., et al. 1999, ApJ, 517, 565 [Google Scholar]
- Piedipalumbo, E., Vignolo, S., Feola, P., & Capozziello, S. 2023, Phys. Dark Univ., 42, 101274 [Google Scholar]
- Plummer, M. 2003, Proceedings of the 3rd International Workshop on Distributed Statistical Computing, Vienna, Austria, 124, 1 [Google Scholar]
- Price-Whelan, A. M., Lim, P. L., Earl, N., et al. 2022, ApJ, 935, 167 [NASA ADS] [CrossRef] [Google Scholar]
- Quinlan, J. R. 1986, Mach. Learn., 1, 81 [Google Scholar]
- Rumelhart, D. E., Hinton, G. E., & Williams, R. J. 1986, Nature, 323, 533 [Google Scholar]
- Sapone, D., & Nesseris, S. 2024, ArXiv e-prints [arXiv:2412.01740] [Google Scholar]
- Scargle, J. D. 1982, ApJ, 263, 835 [Google Scholar]
- Schapire, R. E. 1990, Mach. Learn., 5, 197 [Google Scholar]
- Schmidt, B. P., Suntzeff, N. B., Phillips, M., et al. 1998, ApJ, 507, 46 [NASA ADS] [CrossRef] [Google Scholar]
- Schwarz, G. 1978, Ann. Stat., 6, 461 [Google Scholar]
- Scolnic, D., Brout, D., Carr, A., et al. 2022, ApJ, 938, 113 [NASA ADS] [CrossRef] [Google Scholar]
- Sharma, S. 2017, ARA&A, 55, 213 [Google Scholar]
- Skilling, J. 2004, AIP Conf. Proc., 735, 395 [Google Scholar]
- Skilling, J. 2006, Bayesian Anal., 1, 833 [Google Scholar]
- Speagle, J. S. 2020, MNRAS, 493, 3132 [Google Scholar]
- Stetson, P. B. 1996, PASP, 108, 851 [NASA ADS] [CrossRef] [Google Scholar]
- Tada, Y., & Terada, T. 2024, Phys. Rev. D, 109, L121305 [CrossRef] [Google Scholar]
- Van Der Malsburg, C. 1986, Brain Theory: Proceedings of the First Trieste Meeting on Brain Theory, October 1–4, 1984 (Springer), 245 [Google Scholar]
- Vehtari, A., Gelman, A., Simpson, D., Carpenter, B., & Bürkner, P.-C. 2021, Bayesian Anal., 16, 667 [CrossRef] [Google Scholar]
- Wang, F.-Y., & Dai, Z.-G. 2006, Chin. Astron. Astrophys., 6, 561 [CrossRef] [Google Scholar]
- Weinberg, S. 1989, Rev. Mod. Phys., 61, 1 [NASA ADS] [CrossRef] [Google Scholar]
- Weller, J., & Albrecht, A. 2002, Phys. Rev. D, 65, 103512 [Google Scholar]
- Yang, W., Pan, S., Vagnozzi, S., et al. 2019, JCAP, 11, 044 [CrossRef] [Google Scholar]
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